AI Research Scientist

About the Position

In this role, you will join the AI and Data Science department within Slingshot's Research and Development (R&D) organization. You will contribute directly to Slingshot's vision to accelerate space sustainability and create a safer, more connected world. You will participate in the identification, development, and integration of novel algorithms and models, leveraging diverse data streams and advanced intelligence engines, and the subsequent integration of those technologies into prototypes and broader AI systems across the Slingshot platform.

Slingshot Aerospace cares deeply about our commitment to company values, mission, and purpose. The core competencies we will be looking to identify include intellectual agility, ability to develop innovative solutions, leadership, performance orientation, and industry expertise.

What You'll Do

• Engage in relevant research and development of AI systems, models, and advanced machine learning algorithms that augment physics-driven modeling and simulation systems
• Explore and implement AI-powered simulation tooling in support of AI workflows through reinforcement learning, multi-agent systems, and hybrid modeling approaches
• Collaborate with research, engineering, and product teams to build AI-powered solutions that meet mission-critical modeling and decision-support needs
• Engage in and support the drafting and review of conference and journal articles and presentations, sharing advances with both internal stakeholders and the wider research community.
• Contribute content totechnical invention disclosures, including associated narrative, graphics, and engagements in support of patent development
• Perform additional responsibilities (no more than 10% of duties) in support of the company's data science and product development initiatives

Requirements

• AI/ML expertise
• Demonstrable experience in the application of AI/ML methodologies including, but not limited to, deep learning, generative models (e.g. LLMs, diffusion models), agentic systems, reinforcement learning, computer vision, or other emerging areas of AI research
• Software development experience
• Familiarity with object-oriented paradigms and functional programming principles
• Expertise in at least one modern high-level programming language (e.g. Python, R, C++, Java)
• Collaborative source code management and maintenance processes (e.g. Github, code reviews, CI/CD)
• Ability to work within multi-disciplinary teams in a fast-paced, evolving operational environment that spans military, government, and industry partners
• Excellent verbal and written communication skills
• Passion for Space and AI/ML applications
• Must be a U.S. citizen eligible for government clearances

Preferred Qualifications

• Experience with fine-tuning LLMs, prompt engineering, retrieval-augmented generation (RAG), and domain adaptation for scientific/engineering datasets using modern ML frameworks and model hubs
• Familiarity with Reinforcement Learning (RL) and multi-agent reinforcement learning to enable training agents that learn strategies in simulation and real-world contexts
• Practical understanding of neural networks, transformer architectures, attention mechanisms, and optimization methods to extend or fine-tune transformer-based models
• Experience building reusable internal tools (connectors, simulation frameworks, evaluation harnesses) to refine simulation-based datasets for AI agent training in support of research, data-driven insights/analytics, and model development
• Hands-on experience supporting the development and deployment of supervised and/or unsupervised learning models
• One or more peer reviewed articles, conference papers, and/or presentations in a science or engineering discipline
• Demonstrable combined experience indicative of skillsets required to utilize APIs, microservices, and workflows that merge physics simulation engines with AI training pipelines
• Familiarity with common agentic protocols (MCP, A2A, etc...)
• Practical working experience with physics-based simulation, and statistical methods (e.g. monte Carlo methods, probabilistic modeling, and Bayesian methods)
• Working knowledge of parallel computing, GPU acceleration, and performance optimization for simulations and training workloads.
• Experience with space and astrodynamics is valuable but not required

These skills are guidelines, not hard and fast rules. You don't have to meet every qualification listed - if your skills are transferable and you meet the minimum requirements, we encourage you to apply.

Location: Remote, US

Classification: Full-time Exempt (computer professional exemption)